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Machine learning analysis demonstrates probabilistic classification of 227 uncertain-type blazars in 5BZCAT, suggesting resolved sub-type assignments across multi-wavelength catalogs.
The fifth edition of the Roma-BZCAT catalog (5BZCAT) contains 227 blazars of the uncertain type (BZU), whose available data do not support an unambiguous assignment to the BL Lacertae object (BZB), flat-spectrum radio quasar (BZQ), or host-galaxy-dominated blazar-like source (BZG) categories. We construct a machine-readable value-added catalog for these sources using 1151 BZB, 1909 BZQ, and 274 BZG catalog entries as the supervised reference sample. The compact input set contains redshift, optical magnitude, radio, X-ray, and gamma-ray measurements; missingness indicators; and spectral slopes connecting the radio, optical, and X-ray bands. Redshift and its availability are explicitly treated as optical-spectroscopy-related label proxies. Cross-validation shows that the Light Gradient Boosting Machine (LightGBM) provides the most reliable probability scores among the tested models, although the stacking ensemble gives a slightly higher balanced accuracy. The final catalog reports LightGBM probabilities and contains 61 BZB-like, 120 BZQ-like, and 46 BZG-like sources; 17 are flagged for low probability separation. These probabilities quantify similarity to existing 5BZCAT labels conditional on catalog measurements, rather than an independent physical taxonomy.
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Zhu et al. (2026) studied this question.
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